Mechanochemical synthesis of various metal nanoparticles and catalytic oxidation of styrene using iron oxide nanoparticles and nanoshells
Bibliographic record
Abstract
Recent research on metal nanoparticles (M NPs) has shown vast potential and rapid advancement in various applications such as luxury consumer goods, industrial catalysts, and public health products. This research has shown that the properties of M NPs are unique from those of the atomic or even bulk counterparts. While the properties of M NPs are very exciting and inspire many research groups, the impact of the synthesis of these materials on the environment is often neglected in the search for purer products. The application of Green Chemistry principles would help to ameliorate some of the negative impact that the development and application of nanomaterials might have on the environment and society. This thesis examines the synthesis of a variety of M NPs through a solid-state, bottom-up, mechanochemical means. An efficient and atom-economical method of synthesizing ultra-small gold NPs is presented whereby the gold (III) precursor is ground directly with the amine stabilizing ligand. It was shown that the stainless steel container used for the reaction proved to be essential for the metal precursor reduction and subsequent NP formation. A more general method of mechanochemical synthesis of M NPs was then presented, whereby lignin, a biomass waste product, was used as reducing agent, ligand, and support material for the NPs. We found the synthesis to be applicable to a wide range of metal precursors and allowed for control over the positioning of the M NPs in the support. We pursued studies on the antimicrobial properties of silver NPs created through this method and found it effective against various bacteria. Another application discussed in the thesis is the catalytic oxidation of styrene using molecular oxygen and a variety of iron and iron oxide NPs and nanoshells (NSs) as catalysts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".